There are multiple approaches to tackle the task of nested named entity recognition.
One class of method addresses the problem by enumerating all possible spans of the input sequence and classify each one with its label, including a "no entity" class. [1]} classify each span independently by pooling its tokens reprentations. [2]} consider the left and right context when classifying the spans. [3]} uses an LSTM cell [4]} to model dependencies between spans that differ by one token. [5]} first filters candidate mentions by predicting all possible start and end tokens and then predicting a label for every mention that starts or end at one of the boundaries.
